Performance Analysis Using the Visual Studio Code Profilers

Code profiling is dynamic program analysis for identifying performance problems, memory, and time complexity of a program, CPU utilization, and so forth. Profiling tools provide measurements of how long a method takes to execute, how often it is called, where it is called from, and how much of total time at is spent executing that method. By using profiling tools, a developer can view the state of the application, accumulate data about variable values, function calls, memory leaks and events traced during code debugging. Collected data from the profiling tool could be used for performance improvement during software development. Visual Studio 2017 supports the following list of tools for profiling and performance testing of your code.

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Using the Visual Studio Code Refactoring Tools

Code refactoring is a controller technique or process of restructuring your code without changing its external behavior for easier maintenance, better readability, understand and extend support. The main purpose of refactoring is to fight technical debt. Code refactoring transforms a mess into clean code and simple design. Visual Studio 2017 provides its own refactoring capabilities, also it supports a list of third party tools for code refactoring.

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Improving Code Quality with Visual Studio Code Metrics

Visual Studio 2017 code metrics is a set of important software tools for measuring the quality, code maintainability, and complexity of code written by a developer. These metrics also provide developers better insight into the code they have written by understanding the types of classes/methods/modules that should be reworked and thoroughly tested, identify potential risks, current state of code, track progress of development, and so forth. Code metrics are available with the VS Community, Premium, and Ultimate editions. Developers also can download the extension and install it in Visual Studio. It's a good practice to use code metrics to validate code; it will not only improve code quality but also make your application maintainable and less complex.

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Visual Studio Diagnostic Tools Breakdown

Performance of any application is top priority from a customer perspective. For a developer, identifying performance issues prior to production deployment is a difficult task indeed. Visual Studio 2015 Diagnostic Tools made it easier for developers to have a performance analysis and review before the application goes live. As a Visual Studio 2015 developer, you can get detailed application insights while you are debugging code.

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Integrating NLog with Visual Studio

NLog is an open source logging framework that enables you to add high-quality logs and sophisticated tracing for your .NET application. It's a free framework, cross-platform, and easy to configure and extend. NLog comes with excellent support for log routing and management capabilities. NLog can log your application data and create logs regardless of the size and complexity of your application.

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Creating and Reading Application Logs in .NET

An error-free piece of code theoretically should exist, but, in reality, it's almost impossible to test whether an application works under every situation. When errors occur, there are different ways to handle them. As a developer, it's best practice to detect and catch errors and log them as well. For example, performance problems may occur only when your application is dealing with a large data load. To diagnose these errors in a production environment, you need to log errors automatically so they can be reviewed and analyzed at a later time to fix the issue. The .NET Framework provides a wide range of logging options; Windows System Event Log is one of those from your .NET application. As a developer, you can write code to log an error in the Event viewer and gather information about the software problems and monitor system events.

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Integrating Adaptive Cards with the .NET Bot Framework

Adaptive Cards is an open platform card exchange format for developers. It provides developers a new way to compose UX (user experiences) through multiple devices. An adaptive card is a piece of UI (HTML, CSS, and JavaScript) defined by the developer and rendered by the native app (bot application) to display required content to the end user. The Bot Framework connector renders these cards through different channels, such as Skype, Facebook, Emulator, WebChat, Slack, Kik, Telegram, SMS, GroupMe, Email, Bing, and so forth. An Adaptive Card can contain text, speech, images, buttons, and input fields created using the JSON format. Following the NuGet library implements classes for building and serializing adaptive card objects in the .NET platform.

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Tapping into OpenCV to Process Images

OpenCvSharp is a cross-platform .NET wrapper of the OpenCV library used for image processing and computer vision algorithms from languages such as C#, VB.NET, and others. OpenCvSharp works on the Windows and Linux (using Mono) platforms. It's modeled on the native OpenCV C/C++ API style; that's why it doesn't force developers to write object-oriented code and allows you to make native-style OpenCV function calls. Comparing OpenCvSharp with EmguCV, I believe OpenCvSharp is the simpler and better supported platform.

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Sending Emails Using the Office 365 APIs

Developers now can send e-mails using Exchange Online as an SMTP server configured in the configuration file. Office 365 APIs also provide options to developers for sending e-mails from devices/applications. Microsoft Office 365 API Tools for Visual Studio enable developers to integrate Office 365 services into their applications. Office 365 APIs can be accessed by using libraries available for server-side .NET, client-side JavaScript, and SDKs. 

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Access Office 365 REST APIs Using .NET Libraries

Microsoft's new Cloud offering of Office 365 APIs is very popular now a days among developers. Developers can access the Office 365 REST APIs by using libraries available for .NET and JavaScript. Along with these libraries, the Visual Studio 2017 release also brings some key updates to developers for the tooling experience, making it easier to interact with Office 365 services. 

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Integrating Cortana with the Universal Windows Platform

Three mobile platforms, iOS, Android, and Windows, has been improving their digital assistants ("Siri" on iOS platform, "Google Now" on Android, and "Cortana" on Windows) very frequently. All these digital assistants offer search device data and Cloud-based information. Cortana's voice command features integrated into Windows 10 enable developers to give users convenient ways to access data. Cortana offers integration into Bing, Office 365, and local data sources. With a wide range of features, Cortana helps users organize and manage their data easily. Cortana also exposes an interface to developers that permits applications to integrate into the Cortana search experience. In this article, I'll walk through you Cortana integration with the Universal Windows Platform (UWP) travel app.

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Building a Speech-enabled Bot with Cortana Skill

Microsoft has launched a private preview of Cortana Skills Kit, which has enabled developers to easily create new Cortana skills. Microsoft also has launched the Cortana Devices SDK, which is currently in private previewand will be available broadly by end of 2017. The Cortana SDK lets OEMs integrate Cortana directly into their devices. The Cortana Devices SDK looks like it will help deliver Cortana to all kinds of new devices.

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Programming Devices and Activities from the Microsoft Graph APIs

Project Rome has solved this user experience and seamless integration problem. Microsoft Graph and Windows 10 will deliver the ability to pick up activities between devices with no hassle. The key benefits for developers with Project Rome are driving adoption and installation of app across all these devices.

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Emgu Integration with .NET: Integrating Images

OpenCV is a very popular library written in the native C and C++ programming languages for processing images. Programmers familiar with the C# and Visual Basic.NET languages often face challenges in using OpenCV. Luckily for C# developers, a cross platform .NET wrapper to the OpenCV image processing library is available; it's named Emgu CV. This wrapper allows C# programs to communicate with native APIs of the underlying library that was written with unmanaged code. Emgu CV functions can be called from languages such as C#, VB, VC++, Python, and so forth. This wrapper can be compiled in Mono and run on Windows, Linux, Mac OS X, iPhone, iPad and Android devices.

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Working with the Microsoft Graph REST APIs

Microsoft Graph exposes Cloud services data, such as Outlook mail, calendar, One Drive, tasks, groups, SharePoint, and so forth. Cloud data could be accessed from a JavaScript client application. A developer needs to set up your Office 365 development environment before accessing Cloud services.

To extract data, follow these steps:

Register the application.
Get an access token for authentication.
Access the Microsoft services data over the Graph API.

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Reading Text from Images Using C#

By using Optical Character Recognition (OCR), you can detect and extract handwritten and printed text present in an image. The API works with different surfaces and backgrounds. To use OCR as a service, you have to get a subscription key from the Microsoft Cognitive Service portal. Steps of registration and obtaining the API keys are mentioned in my previous article, "Analyzing Image Content Programmatically: Using the Microsoft Cognitive Vision API."

In this post, I will demonstrate handwriting and text recognition by uploading a locally stored image consuming the Cognitive API.

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What Is Microsoft Graph?

Microsoft Graph is a developers' API platform to connect to the data that drives productivity. It's built on top of Office 365 and allows developers to integrate their services with Azure AD, Excel, Intune, Outlook, One Drive, OneNote, SharePoint, Planner, and other Microsoft products. You can build smarter productivity apps by using Microsoft Graph. Microsoft Graph gives you a single REST API endpoint to interact with Office 365 products. It delegates calls to different Office 365 Cloud services via one single endpoint: https://graph.microsoft.com. You can use the Microsoft Graph API to interact with the data of millions of users present in Microsoft Cloud.

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Using Microsoft Cognitive Services to Add Facial Recognition to Your Apps

In my first article of the Cognitive Face API series, "Adding Facial Recognition to Your Apps," I explained the process of face detection, features, subscription steps, and API service calls. In this tutorial on the Face API, I will create a Universal Windows Platform (UWP) app consuming the Microsoft Azure Cognitive Services API to demonstrate some of the characteristics of face recognition.

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Adding Facial Recognition to Your Apps

The Microsoft Cognitive Services Face API is a Cloud-based service with the most advanced machine learning algorithms. It offers detection of faces in images with attributes, extract features such as gender and age, organize images into groups, and identify individuals. The Ace API can detect up to 64 faces with high precision face location in an image. You have to specify image by bytes or a valid image URL. Cognitive Services even has an API for detecting individuals' emotions based on their facial expressions. Microsoft is currently offering a free plan with limited calls of 30,000 transactions per month. To calculate pricing above the mentioned number of free transaction, please visit URL given.

Read my article for details..http://www.codeguru.com/csharp/azure/analyzing-image-content-programmatically-using-the-microsoft-cognitive-vision-api.html 

Analyzing Image Content Programmatically: Using the Microsoft Cognitive Vision API

Microsoft Cognitive Computer Vision APIs are Azure, Cloud-based, powerful REST services. These APIs provide developers access to advanced machine learning algorithms for processing images and returning back the image analysis in a JSON-structured format. Cognitive Vision APIs help developers to build powerful intelligence into the applications to enable natural and contextual interactions. Computer Vision APIs could be used for information such as image description, recognizing celebrities, reading text from images, and generating thumbnails. The Computer Vision API can tag images according to their content. Read my article for details..

http://www.codeguru.com/csharp/azure/analyzing-image-content-programmatically-using-the-microsoft-cognitive-vision-api.html 

ArchiMate, BPMN and UML - Which one to use for EA

https://eavoices.com/2011/01/27/archimate-bpmn-and-uml-together/

https://blogs.msdn.microsoft.com/nickmalik/2009/04/17/will-there-be-a-battle-between-archimate-and-the-uml/